The automotive industry is about to go through major changes thanks to recent huge improvements in AI, especially generative AI. From coming up with new car features to streamlining how cars are made to offering better customer experiences, it’s clear generative AI has huge potential to shake things up. But even with all the promise, bringing generative AI into the automotive industry comes with major hurdles that need clearing.
We’ll look at the core hurdles that automotive companies must overcome to integrate generative AI into their operations successfully. We will explore the complexities surrounding data management, model development, safety and regulatory compliance, talent acquisition, and setting up the tech infrastructure needed to make it all work. The goal is to give the industry a roadmap to work through these speed bumps so they can tap into the huge upside of this game-changing technology.
- Gen AI Adoption Challenges for Automotives
- Ensuring Data Accuracy, Completeness, and Proper Annotation to Train the AI Models
According to research conducted by Great Expectations, a leading open-source platform for data quality, the story of data quality is grim with 77% of organizations admitting hitting a wall when it comes to data quality and a whopping 91% saying that this directly affects their overall performance. With 13% of respondents confessing little or no confidence in the quality and accuracy of their data, trust in data remains a top issue. It also takes high-quality, properly labeled data to effectively train AI models for automotive companies. Subpar AI performance such as an increase in safety hazards and declining operational efficiency can be attributable to low data quality, negating the opportunities that AI offers for areas like autonomous driving and predictive maintenance.
- Protecting Sensitive Customer and Vehicle Data
According to Mozilla’s “Privacy Not Included” survey, conducted across 25 leading car brands, all of them are reported to collect an excessive amount of personal data from drivers:
- All surveyed top car brands collect an excessive amount of personal data.
- A staggering 84% of them share or sell this data.
- An alarming 92% provide drivers with little to no control over their personal data.
As consumer privacy has become a bigger concern, automakers are under significant scrutiny about how and what data they collect. They have to hit the right balance between using data for innovation and protecting privacy rights. Using robust encryption standards, transparent data usage guidance, and adoption of more privacy-driven data acquisition practices will inspire consumer trust and support regular adherence to new regulatory standards.
Failure to do so could not only deter consumers but also lead to serious privacy breaches, resulting in hefty fines and lawsuits—ultimately jeopardizing the trust needed for AI technology adoption in the automotive industry.
- Understanding Explainability to Know How the AI Models Are Making Decisions
Most Gen AI models, especially those controlling high-level functions such as self-driving cars run by the major automakers, are “black boxes.” It can be difficult to understand why a certain action is taken as the decision-making process is frequently non-transparent.
The second problem with this implementation is that you gain no insight into what the model is truly doing, which can be quite a challenge in safety-critical applications. To take an example, although self-driving cars have many advantages over human-driven ones since they are involved in 2X as many accidents per million miles as same-year conventional vehicles, this has led to concerns about the safety consequences of using black-box AI. We need to have more accountability and understanding behind model decisions, particularly with critical operations.
- Seamless Integration of AI Systems into Existing Tech Stack and Ecosystem
A study by The Manufacturer found that 74% of manufacturing and engineering companies are using old systems and spreadsheets to manage their operations even in the post-pandemic environment. This, in turn, leaves them at the jaw-droppingly vulnerable juncture of needing to work with rigid, obsolete, and siloed data that are supposed to be sufficient for corporate decisions. However, with the urgent need for such advancement, the otherwise difficult task of incorporation becomes critical—typically involving a total redesign of legacy systems that may not readily integrate with flexible AI technologies.
- Building and Managing the Necessary IT Infrastructure for AI Models
Deploying AI initiatives demands a strong IT backbone including cloud computing resources, data storage solutions, and real-time processing capabilities. More broadly, automotive organizations will be required to invest much more heavily in modernizing their IT infrastructure to support the substantial computational requirements of AI algorithms. This encompasses not just hardware but also software elements capable of supporting large-scale collection, storage, and analysis of data. As a result, many in the automotive industry are left with having to decide between allocating their resources to infrastructure or AI objectives.
- Building/Hiring a Team of AI Experts Who Understand the Automotive Industry
Another massive hurdle: finding and keeping engineers with a strong understanding of AI and expertise in the automotive industry. This is in line with a Salesforce survey, which found that 60% of IT professionals working within the public sector are finding their sectors to be faced with a shortage of AI skills—and this shortage is acute in specializations.
Moreover, according to Skill-Lync’s “Automotive Talent Trends Report of 2024”, numerous businesses in the global automotive space are facing talent shortages rendering them unable to hire for expertise in software-defined vehicles (SDV) and advanced driver-assistance systems (ADAS). Automotive and manufacturing executives in India (94%) say they have a hard time driving transformation from within. This extends well beyond technical knowledge—understanding industry-specific challenges is fundamental to ensuring AI applications will work in automotive.
- Ensuring Regulatory Compliance
The final hurdle: there is no unifying standard for safety, data privacy, and ethics, which varies greatly in different parts of the world pertaining to AI technologies.
However, it can indeed be difficult to ensure these regulations are followed as the automotive industry is now under significant pressure with the advancing presence of AI in decision-making processes, for example:
- Who bears legal responsibility when an AI-powered self-driving car malfunctions, causing harm—the manufacturer, software developer, or vehicle owner?
- How do current regulatory frameworks address liability for accidents involving autonomous vehicles with faulty AI systems?
- How will international regulations adapt to address liability and safety standards for self-driving cars across borders?
Compliance introduces a layer of complexity on top of the already challenging proposition of deploying AI and mandates constant surveillance and adaptation to changing legal frameworks.
Conclusion
This article describes critical challenges that the automotive sector must face as it recognizes the transformative power of generative AI. These are no small hurdles either, dealing with everything from high-level data quality and customer privacy down to the need for new tech and compliance with regulations. Overcoming these obstacles is crucial to unlocking the power of AI for innovation, motivating an excellent operational sector, and superior experiences with customers.
Do you think that your automotive business is capable enough to overcome these challenges and gain maximum benefits from AI? Our team at Ascentt designed our AI/ML and data science services to help you move past these challenges so that you can unlock the true potential of your data. So, whether you are just beginning to adopt AI or you want to scale your existing capabilities, we can help. Get in touch with Ascentt and find out how we can get your automotive business ready to excel in the AI-centric world.